Find research datasets worth reusing

opencc-by-4.0Jan 2023View details →

2D LSFM timelapse of cardiomyocyte calcium dynamics

Uploaded zip-folder contains the following files: 1. A representative raw dataset of a 2D LSFM ventricular cardiomyocyte undergoing stimulated calcium transients and calcium sparks (frame_0000.tif -frame_17999.tif) 2. The recorded pacing signal time trace (waveform_test.xslx) 3. Image corresponding to the time-averaged background (AVG_19_35_39_LowNA rolling shutter.tif) 4. Pre-processed nuclear mask matrix (NuclearMask.mat), CMO-channel average (CMO_Average.mat), and CMO channel maximum intensity projection (CMO_MIP).  5. Split and co-registered data for each spectral channel (CMO_frame_00001.tif-CMO_frame_18000.tif, FLUO4_frame_00001.tif -FLUO4_frame_18000.tif). Compressed file size: 14.9 GB Uncompressed file size: 42.8 GB.  Related to the following manuscript:  Liuba Dvinskikh, Hugh Sparks, Ken MacLeod and Chris Dunsby " High-speed 2D light-sheet fluorescence microscopy enables quantification of spatially varying calcium dynamics in ventricular cardiomyocytes" (2023), In review with Frontiers in Physiology, Cardiac Electrophysiology.

Centripetal migration in Drosophila ovary I: wild type timelapse & milestones pt1

Timelapse imaging data tracking inward migration of follicle cells during centripetal migration in Drosophila ovary.  Part of data supporting Figs 2, S1 of “Two phases for centripetal migration of Drosophila melanogaster follicle cells: initial ingression followed by epithelial migration” DOI: 10.1242/dev.200492      •Timelapse imaging data of wild type samples      •Quantitative analysis of specific milestone morphologies      •Analysis of distances between leader FC tips from opposite sides of egg chamber prior to Milestone VII in relevant samples

Centripetal migration in Drosophila ovary VII: E-cadherin RNAi clones in follicle cells timelapse

Data supporting Figs. 5, S7, S8, S9, S10, S11, S12, S13, S14, S15 of “Two phases for centripetal migration of Drosophila melanogaster follicle cells: initial ingression followed by epithelial migration” DOI: 10.1242/dev.200492 <ul> <li>“FC RNAi flipout timelapse data complete”               43.53GB</li> </ul>            Timelapse image data for clones of follicle cells with E-Cadherin knockdown <ul> <li>“Live FC RNAi Clonal Data prelim evaluation”             22.1 MB</li> </ul>             Preliminary evaluation of follicle cell E-Cadherin knockdown samples <ul> <li>“RNAi clone M2-M3-M5 quant”                                12 KB</li> </ul>            Quantitative data from specific milestones for clones of follicle cells with E-Cadherin knockdown

opencc-by-4.0Sep 2023View details →

Phenotypic differences between interfertile Chlamydomonas species- focus-filtered timelapse data and measurements

This repository contains focus-filtered timelapse microscopy data of two interfertile Chlamydomonas algal species. The protocol to generate this data is described in the associated publication, <a href="https://doi.org/10.57844/arcadia-35f0-3e16">"Phenotypic differences between interfertile Chlamydomonas species"</a>, and summarized here. Cells were collected from agar plates and suspended in water, then left to sit overnight to encourage gamete formation. During this time, non-motile cells settled, allowing for the enrichment of motile cells in the supernatant. These enriched cells were then loaded onto agar microchambers (100 micron diameter and 40 micron depth) for imaging. We collected videos on a Nikon Ti2-E microscope equipped with a Photometrics Kinetix digital scMos camera. We performed differential interference contrast (DIC) imaging using a Plan Apo 10× 0.45 Air objective. We collected videos with a 5.1 ms exposure with acquisition every 50 ms for three minutes. We placed a red light filter [IR longpass, 610 nm (ThorLabs)] in the light path to maintain swimming behavior of cells. The procedure was standardized and repeated four times to ensure consistency. Focus-filtered timelapse data of C. reinhardtii or C. smithii cells in agar microchamber wells are shared here. The code for focus-filtering and collection of measurements can be found in the <a href="https://github.com/Arcadia-Science/chlamy-comparison">associated Github repository</a>.<h4>Reference</h4><a href="https://doi.org/10.57844/arcadia-35f0-3e16">Essock-Burns T, Garcia III G, MacQuarrie CD, Mets DG, York R. (2023). Phenotypic differences between interfertile Chlamydomonas species</a><h4>Notes</h4>In addition to the raw data, the dataset includes sample images that are intermediates in the image processing pipeline, as well as 2D morphology measurements of the cells in a csv file."Cr" indicates Chlamydomonas reinhardtii"Cs" indicates Chlamydomonas smithiiFrame rate: 20 frames per second (fps)Pixel size: 0.6398 microns/pixel

opencc-by-4.0Nov 2023View details →

Phenotypic differences between interfertile Chlamydomonas species- timelapse microscopy data, part 2

This repository contains timelapse microscopy data of two interfertile Chlamydomonas algal species. The protocol to generate this data is described in the associated publication, <a href="https://doi.org/10.57844/arcadia-35f0-3e16">"Phenotypic differences between interfertile Chlamydomonas species"</a>, and summarized here. Cells were collected from agar plates and suspended in water, then left to sit overnight to encourage gamete formation. During this time, non-motile cells settled, allowing for the enrichment of motile cells in the supernatant. These enriched cells were then loaded onto agar microchambers (100 micron diameter and 40 micron depth) for imaging. We collected videos on a Nikon Ti2-E microscope equipped with a Photometrics Kinetix digital scMos camera. We performed differential interference contrast (DIC) imaging using a Plan Apo 10× 0.45 Air objective. We collected videos with a 5.1 ms exposure with acquisition every 50 ms for three minutes. We placed a red light filter [IR longpass, 610 nm (ThorLabs)] in the light path to maintain swimming behavior of cells. The procedure was standardized and repeated four times to ensure consistency. Timelapse data of C. reinhardtii or C. smithii cells in agar microchamber wells from experiments "3" and "4" are shared here.<h4>Reference</h4><a href="https://doi.org/10.57844/arcadia-35f0-3e16">Essock-Burns T, Garcia III G, MacQuarrie CD, Mets DG, York R. (2023). Phenotypic differences between interfertile Chlamydomonas species</a><h4>Notes</h4>Experiment 3, performed on 230519: DIC timelapse data of Chlamydomonas cells swimming in agar microchamber wells. In some of the wells, external fluid movement modified the cell motility patterns.Experiment 4, performed on 230523: DIC timelapse data of Chlamydomonas cells swimming in agar microchamber wells."Cr" indicates Chlamydomonas reinhardtii"Cs" indicates Chlamydomonas smithiiTimelapse frames: 3601 framesFrame rate: 20 frames per second (fps)Pixel size: 0.6398 microns/pixel

opencc-by-4.0Nov 2023View details →

Phenotypic differences between interfertile Chlamydomonas species- timelapse microscopy data, part 1

This repository contains timelapse microscopy data of two interfertile Chlamydomonas algal species. The protocol to generate this data is described in the associated publication, <a href="https://doi.org/10.57844/arcadia-35f0-3e16">"Phenotypic differences between interfertile Chlamydomonas species"</a>, and summarized here. Cells were collected from agar plates and suspended in water, then left to sit overnight to encourage gamete formation. During this time, non-motile cells settled, allowing for the enrichment of motile cells in the supernatant. These enriched cells were then loaded onto agar microchambers (100 micron diameter and 40 micron depth) for imaging. We collected videos on a Nikon Ti2-E microscope equipped with a Photometrics Kinetix digital scMos camera. We performed differential interference contrast (DIC) imaging using a Plan Apo 10× 0.45 Air objective. We collected videos with a 5.1 ms exposure with acquisition every 50 ms for three minutes. We placed a red light filter [IR longpass, 610 nm (ThorLabs)] in the light path to maintain swimming behavior of cells. The procedure was standardized and repeated four times to ensure consistency. Timelapse data of C. reinhardtii or C. smithii cells in agar microchamber wells from experiments "1" and "2" are shared here.<h4>Reference</h4><a href="https://doi.org/10.57844/arcadia-35f0-3e16">Essock-Burns T, Garcia III G, MacQuarrie CD, Mets DG, York R. (2023). Phenotypic differences between interfertile Chlamydomonas species</a><h4>Notes</h4>Experiment 1, performed on 230509: This experiment was meant to include DIC timelapse data, but a DIC polarizer was not inserted during the data collection. The resulting data was effectively brightfield data.Experiment 2, performed on 230516: DIC timelapse data of Chlamydomonas cells swimming in agar microchamber wells."Cr" indicates Chlamydomonas reinhardtii"Cs" indicates Chlamydomonas smithiiTimelapse frames: 3601 framesFrame rate: 20 frames per second (fps)Pixel size: 0.6398 microns/pixel

opencc-by-4.0Mar 2019View details →

Quantitative phase microscopy timelapse dataset of PNT1A, DU-145 and LNCaP cells with annotated caspase 3,7-dependent and independent cell death

Time-lapse dataset of prostatic cell lines (DU-145, PNT1A, LNCaP) exposed to cell death-inducing compounds (staurosporine, doxorubicin) and black phosphorus. The time-lapse dataset is annotated as follows: (1) cell masks and cell numbers, (2) by cell death type and timepoint of death in the attached xlsx file. This dataset is supplementary to the article: Vicar, T., Raudenska, M., Gumulec, J. et al. The Quantitative-Phase Dynamics of Apoptosis and Lytic Cell Death. Sci Rep 10, 1566 (2020). <a href="https://doi.org/10.1038/s41598-020-58474-w">https://doi.org/10.1038/s41598-020-58474-w</a> Correlative fluorescence microscopy is in a separate dataset <a href="https://doi.org/10.5281/zenodo.4531900">10.5281/zenodo.4531900</a> Code is available at <a href="https://github.com/tomasvicar/CellDeathDetect">https://github.com/tomasvicar/CellDeathDetect</a> Methods Cell culture and cultured cell conditions LNCaP cell line was established from a lymph node metastase of the hormone-refractory patient and contains a mutation in the AR gene. This mutation creates a promiscuous AR that can bind to different types of steroids. LNCaP cells are AR-positive, PSA-positive, PTEN-negative and harbor wild-type p53 {Skjoth, 2006 #150; Mitchell, 2000 #149}. PNT1A is immortalized non-tumorigenic epithelial cell line. PNT1A cells harbour wild-type p53. However, SV40 induced T-antigen expression inhibits the activity of p53. This cell line had lost the expression of androgen receptor (AR) and prostate-specific antigen (PSA) (Raudenska, 2019). DU-145 cell line is derived from the metastatic site in the brain and contains P223L and V274F mutations in p53. This cell line is PSA and AR-negative and androgen independent (Chappell, 2012). All cell lines used in this study were purchased from HPA Culture Collections (Salisbury, UK). and were cultured in RPMI-1640 medium with 10 % FBS. The medium was supplemented with antibiotics (penicillin 100 U/ml and streptomycin 0.1 mg/ml). Cells were maintained at 37°C in a humidified (60%) incubator with 5% CO2 (Sanyo, Japan). Correlative time-lapse quantitative phase-fluorescence imaging QPI and fluorescence imaging were performed by using multimodal holographic microscope Q-PHASE (TESCAN, Brno, Czech Republic). To determine the amount of caspase-3/7 product accumulation, cells were loaded with 2 µM CellEventTM Caspase-3/7 Green Detection Reagent (Life Technologies, Carlsbad, CA, USA) according to the manufacturer’s protocol and visualized using FITC 488 nm filter. To detect the cells with a loss of plasma membrane integrity, cells were stained with 1 ug/ml propidium iodide (Sigma Aldrich Co., St. Louis, MO, USA) and visualized using TRITC 542 nm filter. Nuclear morphology and chromatin condensation were analyzed using Hoechst 33342 nuclear staining (ENZO, Lausen, Switzerland) and visualized using DAPI 461 nm filter. Cells were cultivated in Flow chambers μ-Slide I Lauer Family (Ibidi, Martinsried, Germany). To maintain standard cultivation conditions (37°C, humidified air (60%) with 5% CO2) during time-lapse experiments, cells were placed in the gas chamber H201 - for Mad City Labs Z100/Z500 piezo Z-stages (Okolab, Ottaviano NA, Italy). To image enough cells in one field of view, lens Nikon Plan 10/0.30 were chosen. For each cell line and each treatment, seven fields of view were observed with the frame rate 3 mins/frame for 24 or 48 h respectively. Holograms were captured by CCD camera (XIMEA MR4021 MC-VELETA), fluorescence images were captured using ANDOR Zyla 5.5 sCMOS camera. Complete quantitative phase image reconstruction and image processing were performed in Q-PHASE control software. Cell dry mass values were derived according to {Prescher, 2005 #177} and {Park, 2018 #178} from the phase (eq. (1)), where m is cell dry mass density (in pg/μm2), φ is detected phase (in rad), λ is wavelength in μm (0.65 μm in Q-PHASE), and α is specific refraction increment (≈0.18 μm3/pg). All values in the formula except the Phi are constant. Phi (Phase) is the value measured directly by the microscope. Integrated phase shift through a cell is proportional to its dry mass, which enables studying changes in cell mass distribution (Park et al., 2018). File description There are three archives included for particular cell lines: <ul> <li>QPI_annotated_timelapse_DU145.zip for DU-145 cells</li> <li>QPI_annotated_timelapse_PNT1A.zip for PNT1A cells</li> <li>QPI_annotated_timelapse_LNCaP.zip for LNCaP cells</li> </ul> The archive includes of following files: <ul> <li>Tiff with time-lapse quantitative phase image (32-bit files 600x600px with values in pg/um2 with framerate 1 frame/3minutes with 1.59 px/um), named QPI_cellline_treatment_FOV.tiff</li> <li>Tiff file with segmentation mask for particular cells named mask_cellline_treatment_FOV.tiff</li> <li>xlsx table with cell death type (1 for apoptosis, 2 for necrosis, 3 for ambiguous/surviving) and time of death for representative cell number from mask, named  labels_cellline_treatment_FOV.xlsx</li> </ul> file naming has following conventions: <ul> <li>cell names: DU145, PNT1A, LNCaP for particular cell line</li> <li>treatments: st, bp, do for staurosporine, black phosphorus and doxorubicin</li> <li>fields of view: 1 to 7</li> </ul> e.g. QPI_DU145_st_4.tif, mask_DU145_st_4.tif, labels_DU145_st_4.xlsx Note that correlative fluorescence images are available at <a href="https://doi.org/10.5281/zenodo.4531900">10.5281/zenodo.4531900</a>

Centripetal migration in Drosophila ovary VI: stretch cell timelapse pt4

Part of data supporting Figs 4, S4,S5 of “Two phases for centripetal migration of Drosophila melanogaster follicle cells: initial ingression followed by epithelial migration” DOI: 10.1242/dev.200492 Data file description: <ul> <li>“MyrtdEOS”                                                                         25.1GB</li> </ul>              Timelapse image data for marked stretch cells <ul> <li>“PG150 Gal4 pt2”                                                               21.78GB</li> </ul>              Timelapse image data for marked stretch cells <ul> <li>“Stretch Cell Analysis CSV files”                                         56 KB</li> </ul>            Preliminary evaluation of stretch cell samples, and quantitative data for stretch cell extensions

Centripetal migration in Drosophila ovary V: stretch cell timelapse pt3

Part of data supporting Figs 4, S4 of “Two phases for centripetal migration of Drosophila melanogaster follicle cells: initial ingression followed by epithelial migration” DOI: 10.1242/dev.200492 Data files descriptions: <ul> <li>“A90 Gal4”                                                                           4.8 GB</li> </ul>              Timelapse image data for marked stretch cells <ul> <li>“C415 Gal4 pt2”                                                                 39.86 GB</li> </ul>             Timelapse image data for marked stretch cells <ul> <li>“Stretch Cell Analysis CSV files”                                         56 KB</li> </ul>              Preliminary evaluation of stretch cell samples, and quantitative data for stretch cell extensions

Centripetal migration in Drosophila ovary IV: stretch cell timelapse pt2

Part of data supporting Figs 4, S4 of “Two phases for centripetal migration of Drosophila melanogaster follicle cells: initial ingression followed by epithelial migration” DOI: 10.1242/dev.200492 Data files descriptions: <ul> <li>“C415 Gal4 pt1”                                                                 45.32 GB</li> </ul>             Timelapse image data for marked stretch cells <ul> <li>“Stretch Cell Analysis CSV files”                                         56 KB</li> </ul>             Preliminary evaluation of stretch cell samples, and quantitative data for stretch cell extensions

Centripetal migration in Drosophila ovary III: stretch cell timelapse pt1

Part of data supporting Figs 4, S4 of “Two phases for centripetal migration of Drosophila melanogaster follicle cells: initial ingression followed by epithelial migration” DOI: 10.1242/dev.200492 Data files descriptions: <ul> <li>“PG150 Gal4 pt1”                                                               35.92 GB</li> </ul>             Timelapse image data for marked stretch cells <ul> <li>“Stretch Cell Analysis CSV files”                                        56 KB</li> </ul>             Preliminary evaluation of stretch cell samples, and quantitative data for stretch cell extensions

opencc-by-4.0Apr 2023View details →

Timelapse footage of deep convective clouds in New Mexico produced during the DCMEX field campaign

Timelapse footage of clouds taken during the<a href="https://cloudsense.ac.uk/dcmex/"> Deep Convective Microphysics Experiment</a> (DCMEX) research project, funded by the UK Natural Environment Research Council. Cameras were pointed towards the Magdalena mountains. The main camera location was Socorro airport, a secondary location was Econolodge, Socorro, and a third location was Magdalena airport. These are a subset of the footage collected. 20s interval photographs from a number of days are available from another archive. During these timelapses the FAAM aircraft was flying through the clouds collecting thermodynamic, dynamics, microphysics and aerosol <a href="https://catalogue.ceda.ac.uk/uuid/b1211ad185e24b488d41dd98f957506c">measurements</a>. Radars were also sometimes operational. The full campaign and data is decribed in detail in <a href="https://essd.copernicus.org/articles/16/2141/2024/">Finney et al. (2024) ESSD</a>. Video descriptions 19th July - A number of convective cloud bursts, but no anvil formed over the Magdalena mountains during this footage. 23rd July - Cumulus are present and growing from the start of the footage. Cloud bases stay rooted to the mountain. Shear appears low until a detrainment layer is reached. 27th July - Footage begins with clear skies. Strong low levels winds carry clouds northward. Deep convective clouds form and are detrained south westward. Later in the footage scene becomes overcast with high cloud, and this suppresses the earlier deep convection. Late in the camera 1 footage a gust front cloud passes across the scene. 29th July - Footage taken from the Magdalena airport and begins with fast moving cumulus cloud. The cloud grows and moves over the camera. 31st July - Cumulus clouds form almost immediately over the Magdalena mountains and steadily grow from multiple thermals. Cloud bases stay rooted to the mountain, but shear aloft carries cloud northward from a detrainment layer. The cloud takes a structure similar to anvil but winds are strong and cloud in the detrainment layer appears to be fairly long-lived, warping the anvil shape somewhat. 2nd August - Footage begins overcast but clears. Cumulus clouds and congestus begin to form, with strong low level winds carrying them southward. Related datasets Facility for Airborne Atmospheric Measurements; Finney, D.; Blyth, A.; Gallagher, M.; Wu, H.; Nott, G.J.; Biggerstaff, M.; Sonnenfeld, R.G.; Daily, M.; Walker, D.; Dufton, D.; Bower, K.N.; Boeing, S.; Choularton, T.W.; Crosier, J.; Groves, J.; Field, P.; Coe, H.; Murray, B.J.; Lloyd, G.; Marsden, N.A.; Flynn, M.; Hu, K.; Thamban, N.M.; Williams, P.I.; Connolly, P.J.; McQuaid, J.B.; Robinson, J.; Cui, Z.; Burton, R.R.; Carrie, G.; Moore, R.; Abel, S.J.; Tiddeman, D.; Aulich, G.; Bennecke, D.; Kelsey, V.; Reger, R.S.; Nowakowska, K.; Bassford, J.; Morris, F.; Hampton, J. (2022): DCMEX: Collection of in-situ airborne observations, ground-based meteorological and aerosol measurements and cloud imagery for the Deep Convective Microphysics Experiment. NERC EDS Centre for Environmental Data Analysis, 30 April 2024. <a href="https://dx.doi.org/10.5285/B1211AD185E24B488D41DD98F957506C">https://dx.doi.org/10.5285/B1211AD185E24B488D41DD98F957506C</a> Individual image archive for camera 1 - Finney, D.; Groves, J.; Walker, D.; Dufton, D.; Moore, R.; Bennecke, D.; Kelsey, V.; Reger, R.S.; Nowakowska, K.; Bassford, J.; Blyth, A. (2023): DCMEX: cloud images from the NCAS Camera 11 from the New Mexico field campaign 2022. NERC EDS Centre for Environmental Data Analysis, 15 December 2023. <a href="https://dx.doi.org/10.5285/b839ae53abf94e23b0f61560349ccda1">https://dx.doi.org/10.5285/b839ae53abf94e23b0f61560349ccda1</a> Individual image archive for camera 2 - Finney, D.; Groves, J.; Walker, D.; Dufton, D.; Moore, R.; Bennecke, D.; Kelsey, V.; Reger, R.S.; Nowakowska, K.; Bassford, J.; Blyth, A. (2023): DCMEX: cloud images from the NCAS Camera 12 from the New Mexico field campaign 2022. NERC EDS Centre for Environmental Data Analysis, 15 December 2023. <a href="https://dx.doi.org/10.5285/d1c61edc4f554ee09ad370f6b52f82ce">https://dx.doi.org/10.5285/d1c61edc4f554ee09ad370f6b52f82ce </a> Oklahoma University radar data - <a href="https://doi.org/10.5281/zenodo.8051426">https://doi.org/10.5281/zenodo.8051426</a>

opencc-by-4.0Jun 2023View details →

Research data supporting: "Detecting dynamic domains and local fluctuations in complex molecular systems via timelapse neighbors shuffling"

This repository contains the set of data shown in the paper "Detecting dynamic domains and local fluctuations in complex molecular systems via timelapse neighbors shuffling" published on PNAS (DOI: 10.1073/pnas.2300565120).

dryad40/100

Sunflower timelapse scoring

Open the record for dataset details and reuse information.

publicJan 2023View details →

opencc-by-4.0Dec 2020View details →

Fiji/CellProfiler cell migration timelapse data set and code

This zenodo upload consists of: Fiji IJMacro script create_LabelledMasks.ijm CellProfiler pipeline TrackMate_CellProfiler.cppipe Matlab script Plot_per_cell.m  Saved manually curated TrackMate project crop_1_60_ManualCuration.xml Saved Spots Results Table of manually curated TrackMate project: Spots in tracks statistics.csv CellProfiler pipeline output cp_output.zip Example data set crop_1_60.tif - subset of image data previously described in: <a href="https://www.zotero.org/google-docs/?9YEDuq">Shafqat-Abbasi, H., Kowalewski, J. M., Kiss, A., Gong, X., Hernandez-Varas, P., Berge, U., Jafari-Mamaghani, M., Lock, J. G., and Strömblad, S. 2016. An analysis toolbox to explore mesenchymal migration heterogeneity reveals adaptive switching between distinct modes. eLife 5:e11384–e11384.</a> Many thanks to Staffan Strömblad et al. for sharing the data.

opencc-by-4.0Mar 2017View details →

Timelapse sequences of radish and tomato growth from seed

These files accompany a schools plant imaging workshop, created by Hannah Dee and colleagues as part of EPSRC Grant EP/LO17253/1. The aim of the workshops is to introduce school-aged pupils to the concepts of plant imaging, and to image processing by building up a set of python programs that can open and investigate properties of images. The images in question are images of plants as they grow. Workshop one concerns the creation of such datasets and can be found here: https://docs.google.com/document/d/1tXNXWhs49XUKWZl5s51mYF4Rpl44P00eih7Ulr8CHvw/edit# Workshop two concerns investigating light changes in such datasets, introducing the concept of a "colour space" and can be found here: https://docs.google.com/document/d/1cAqEZQ-llwgvifhOiNROsFp91AaMjjU0qJOn5F7wb5o/edit# These files allow students to skip workshop one and use our pre-recorded timelapse sequences. Further workshops are planned and will be linked from the google docs above when completed.

opencc-by-4.0Nov 2023View details →

Microchamber slide design for cell confinement during imaging- Tetrahymena rostrata timelapse data

We performed Imaging on a Nikon Ti2-E & Yokogawa CSU W1-SoRa microscope. The microscope was equipped with an ORCA-Fusion BT digital C-MOS camera. We used a 10× 0.45 Plan Apo Air  or a  Plan Apo λD 100x OIL OFN25 DIC N2 objective for differential interference contrast (DIC) imaging. More microscopy information is detailed in the metadata file associated with each .nd2 file. Tetrahymena rostrata was provided by Andrzej Kaczanowski, who isolated the cells from the small snail Cohlicopa lubrica near Warsaw. The cells were cultured in medium consisting 0.5% yeast extract and 0.5% of proteose peptone supplemented with 250 ug/ml streptomycin sulphate and 250 ug/ml penicillin G to maintain sterility.

opencc-by-4.0Jan 2022View details →

Test datasets for segmentation (2D, 3D, timelapses)

Datasets for testing: 1) Arabidopsis in 3D with membrane labelling for testing Unet, Stardist and denoising models.                                  2) Ascadian embryo in 3D imaged via light sheet for testing Unet and Stardist models.                                   3) Carcinoma cells 3D + time data imaged under low SNR conditions for testing Unet, Stardist and Denoising models.                                  4) Tissue in 2D + time for testing Unet models. Data imaged by Mari Tolonen at the University of Copenhagen.                                  5) Nuclei in 3D. Raw, GT and model prediction. Data imaged by Mari Tolonen at the University of Copenhagen.                                   6) Nuclei cell mask 2D. Raw, GT and model prediction. Data imaged by Mari Tolonen at the University of Copenhagen